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Tier-1 brand, metro location and mid-level experience increase competition, but niche GenAI skills limit volume.
Role requires specialized GenAI/LLM and GPU expertise, limiting cross-industry transferability.
Many mandatory GenAI/LLM, GPU, and DevOps skills plus explicit 3+ years requirement make filters strict.
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Design and manage ML pipelines for experiment, model, feature management, and retraining, including API design for scalable model inferencing.
Implement and optimize large language model (LLM) serving with expertise in GPU architectures, distributed training, and frameworks such as DeepSpeed and vLLM.
Apply DevOps and LLMOps best practices including Kubernetes, Docker, and LLM orchestration frameworks like Flowise, Langflow, and Langgraph.
3+ years of professional experience in data analytics, machine learning, or AI-related roles.
Proficiency in Generative AI, LLMs, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Education: Bachelor or Master of Engineering (BE/B.Tech/MEng) or equivalent (MBA/MCA mentioned but not explicitly required).
Not explicitly mentioned: Notice period, work location constraints, or visa sponsorship details.
Experienced in end-to-end ML pipeline design and operational deployment of large-scale AI models in cloud environments (AWS, Azure, GCP).
Strong technical skill set balancing model fine-tuning, optimization, and containerized orchestration for LLM workloads.
Comfortable working with advanced LLM tooling ecosystems (e.g., MLflow, SageMaker, Vertex AI) and capable of handling complex GPU-distributed training setups.